code: def load_data(dtype=np.float32, order="C", shuffle=True, seed=0):
      """Load the data, then cache and memmap the train/test split"""
      print("Loading dataset...")
      data = fetch_openml("mnist_784", as_frame=True)
  
      X = check_array(data["data"], dtype=dtype, order=order)
      y = data["target"]
  
      if shuffle:
          X, y = _shuffle(X, y, random_state=seed)
  
      # Normalize features
      X /= 255
      return X, y
  
file_path: scikit-learn/benchmarks/bench_tsne_mnist.py
imports: import argparse
  import json
  import os
  import os.path
  from time import time
  import numpy
  from joblib import Memory
  from sklearn.datasets import fetch_openml
  from sklearn.decomposition import PCA
  from sklearn.manifold import TSNE
  from sklearn.neighbors import NearestNeighbors
  from sklearn.utils import check_array
  from sklearn.utils import shuffle
  from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
label_desc: Use image node_id 1 for calling a global function with example usage: load_data(dtype, order, shuffle, seed) and returns: X,  y